scholarly journals Spatial Communication Activity in Wireless Sensor Networks Based on Migrated Base Stations

Author(s):  
Jan Nikodem ◽  
Marek Woda ◽  
Maciej Nikodem
2013 ◽  
Vol 347-350 ◽  
pp. 975-979
Author(s):  
Rong Zhao ◽  
Cai Hong Li ◽  
Yun Jian Tan ◽  
Jun Shi ◽  
Fu Qiang Mu ◽  
...  

This paper presents a Debris Flow Disaster Faster-than-early Forecast System (DFS) with wireless sensor networks. Debris flows carrying saturated solid materials in water flowing downslope often cause severe damage to the lives and properties in their path. Faster-than-early or faster-than-real-time forecasts are imperative to save lives and reduce damage. This paper presents a novel multi-sensor networks for monitoring debris flows. The main idea is to let these sensors drift with the debris flow, to collect flow information as they move along, and to transmit the collected data to base stations in real time. The Raw data are sent to the cloud processing center from the base station. And the processed data and the video of the debris flow are display on the remote PC. The design of the system address many challenging issues, including cost, deployment efforts, and fast reaction.


Author(s):  
Kailash Subramanian

Wireless Sensor Networks motes have a small size, which leads to severe power supply restrictions. Much of the work on conserving power has been undertaken in the domain of routing protocols which deals with sending data in an efficient manner. In this paper a new scalar based protocol is proposed with a combination of multiple sub-base stations, that seeks to enhance the efficiency of protocol in terms of consumption of power and node failure tolerance. All the nodes are divided into regions, with each region having a sub- base station(sBS) and an arbitrary scalar value. Each sBS has lesser power supply and computation power compared to main station, but more of the mentioned metrics with respect to the sensor motes. Previous studies have described various paradigms and metrics for routing protocols and the placement of base stations. In this paper, the said algorithm is proposed, and its efficiency is analysed.


2017 ◽  
Vol 13 (11) ◽  
pp. 4 ◽  
Author(s):  
Marouane El Mabrouk ◽  
Salma Gaou

A wireless sensor network is a network that can design a self-organizing structure and provides effective support for several protocols such as routing, locating, discovering services, etc. It is composed of several nodes called sensors grouped together into a network to communicate with each other and with the base stations. Nowadays, the use of Wireless sensor networks increased considerably. It can collect physical data and transform it into a digital values in real-time to monitor in a continuous manner different disaster like flood. However, due to various factors that can affect the wireless sensor networks namely, environmental, manufacturing errors hardware and software problems etc... It is necessary to carefully select and filter the data from the wireless sensors since we are providing a decision support system for flood forecasting and warning. In this paper, we presents an intelligent Pre-Processing model of real-time flood forecasting and warning for data classification and aggregation. The proposed model consists on several stages to monitor the wireless sensors and its proper functioning, to provide the most appropriate data received from the wireless sensor networks in order to guarantee the best accuracy in terms of real-time data and to generate a historical data to be used in the further flood forecasting.


Author(s):  
Dina M. Ibrahim ◽  
Nada M. Alruhaily

With the rise of IOT devices and the systems connected to the internet, there was, accordingly, an ever-increasing number of network attacks (e.g. in DOS, DDOS attacks). A very significant research problem related to identifying Wireless Sensor Networks (WSN) attacks and the analysis of the sensor data is the detection of the relevant anomalies. In this paper, we propose a framework for intrusion detection system in WSN. The first two levels are located inside the WSN, one of them is between sensor nodes and the second is between the cluster heads. While the third level located on the cloud, and represented by the base stations. In the first level, which we called light mode, we simulated an intrusion traffic by generating data packets based on TCPDUMP data, which contain intrusion packets, our work, is done by using WSN technology. We used OPNET simulation for generating the traffic because it allows us to collect intrusion detection data in order to measure the network performance and efficiency of the simulated network scenarios. Finally, we report the experimental results by mimicking a Denial-of-Service (DOS) attack. <em> </em>


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